Making the case for digital twins: Italian healthcare needs AI-driven predictive modeling for personalized medicine

Abstract

Precision medicine seeks to tailor care by integrating genetic, clinical, and environmental data. Digital twins, dynamic, virtual replicas of patients that are updated with longitudinal information, represent a significant step in this direction. Enabled by artificial intelligence, they allow in silico experimentation to simulate therapies, disease trajectories, and adverse events, reducing risk and sharpening personalization. By bridging data and decisions, digital twins can promote earlier diagnosis, targeted treatments, and faster drug discovery, supporting a shift from reactive to predictive and participatory care. Nonetheless, challenges surrounding data integration, privacy, regulation, and equity persist and necessitate collaborative solutions. This viewpoint examines the opportunities and system-level requirements to integrate digital twins into Italian healthcare. Digital twins redefine medicine by turning episodic encounters into continuous, adaptive care. They can anticipate clinical events, simulate individualized treatments, and support shared decision-making, advancing the vision of predictive, preventive, personalized, and participatory medicine. Realizing this potential requires robust governance, interoperable infrastructures, and clinician training, alongside ethical frameworks that protect autonomy and fairness. Public-private partnerships and international collaboration will be crucial for the responsible, inclusive, and transparent adoption of these initiatives. Ultimately, digital twins inaugurate a paradigm in which simulation and clinical reality converge, fostering innovation that is both scientifically rigorous and deeply human.

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